Binary response models for heterogeneous panel data with interactive fixed effects

Abstract

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and establish the corresponding asymptotic theory. In addition, we conduct intensive simulations to examine the theoretical findings. In an empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis.